10 Ways to Guarantee More Sales and Conversions What’s the one thing that all businesses have in common? You guessed it, they exist to generate revenue for someone, whether it be shareholders, co-founders, or small-time owners. Of course, the issue with generating revenue is that you’re required to convert leads into customers, which is why so many startups and small businesses focus on things like compelling copy, split testing, and email campaigns. At the end of the day though, no matter how well you court a future customer, there’s really only one thing that they want, and that’s a guarantee that they’ll get something out of the purchase. Some people want results, some want gratification, and some want happiness, but at the end of the day it all boils down to one thing: avoiding regret. People simply don’t want to make a purchase that they’ll regret, and saying no is their way of avoiding just that. How do you do it? Is your guarantee good enough to do that? Standard Guarantees 1. 2. 3. 100% Satisfaction Guarantee How does it work? 4. 5.
Data mining Process of extracting and discovering patterns in large data sets Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.[1][2][3][4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Etymology[edit] Background[edit] The manual extraction of patterns from data has occurred for centuries. Process[edit]
Data Mining Image: Detail of sliced visualization of thirty video samples of Downfall remixes. See actual visualization below. As part of my post doctoral research for The Department of Information Science and Media Studies at the University of Bergen, Norway, I am using cultural analytics techniques to analyze YouTube video remixes. My research is done in collaboration with the Software Studies Lab at the University of California, San Diego. A big thank you to CRCA at Calit2 for providing a space for daily work during my stays in San Diego. The following is an excerpt from an upcoming paper titled, “Modular Complexity and Remix: The Collapse of Time and Space into Search,” to be published in the peer review journal AnthroVision, Vol 1.1. The following excerpt references sliced visualizations of the three cases studies in order to analyze the patterns of remixing videos on YouTube. Image: this is a slice visualization of “The Charleston and Lindy Hop Dance Remix.”
Advanced Segmentation Triples One Brand’s Click Rate, Produces Click-To-Open Rate 2.7 Times Higher Back to Blog November 18th, 2010 by Justin Montgomery As an interesting case study to the importance of segmentation in email marketing, one brand considerably improved the success of its email campaigns by sending out over 130 versions of its newsletter. The brand, “HealthyPet,” which produces email and direct mail appointment reminders and educational content for over 4,000 veterinarians nationwide, launched its advanced segmentation campaign in January 2010, and has since seen its unique click rate triple, and its click-to-open rate go 2.7 times higher than a year ago. HealthyPet decided to segment its database by age of pet and tailor the content of its newsletters accordingly. In addition to creating specific messages depending on the age of the pet, HealthyPet also segmented its list by pet breed , since a German shepherd, for example, is likely to suffer from different health and wellness issues than a Chihuahua.
Relational data mining From Wikipedia, the free encyclopedia Relational data mining is the data mining technique for relational databases.[1] Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional patterns, there are corresponding relational patterns. For example, there are relational classification rules (relational classification), relational regression tree, and relational association rules. There are several approaches to relational data mining: Multi-Relation Association Rules: Multi-Relation Association Rules (MRAR) is a new class of association rules which in contrast to primitive, simple and even multi-relational association rules (that are usually extracted from multi-relational databases), each rule item consists of one entity but several relations. Web page for a text book on relational data mining
Eureqa Eureqa is a breakthrough technology that uncovers the intrinsic relationships hidden within complex data. Traditional machine learning techniques like neural networks and regression trees are capable tools for prediction, but become impractical when "solving the problem" involves understanding how you arrive at the answer. Eureqa uses a breakthrough machine learning technique called Symbolic Regression to unravel the intrinsic relationships in data and explain them as simple math. Over 35,000 people have relied on Eureqa to answer their most challenging questions, in industries ranging from Oil & Gas through Life Sciences and Big Box Retail. Eureqa One Page Overview (.pdf) »Visit the Eureqa Community » Eureqa utilizes a machine learning technique called Symbolic Regression to distill raw data into non-linear mathematical equations.
Five Unique Calls to Action that Will Make You Click Twice The call to action is the “Holy Grail” of every marketer. Get it right, and you’re swimming in sales. Get it wrong, and your traffic tends to stagnate. Address Customer Reluctance Upfront (LightCMS) LightCMS does this wonderfully, although you have to scroll all the way to the bottom of their page to find it. Think Outside the Rectangle (Storenvy) Most call to action buttons are simply rectangles, but some of the highest click-through rates have been reported on buttons that break outside the box. What Happens After I Push It? As enticing as your graphics look, many people don’t convert because they don’t know what will happen after they click. Pique the User’s Curiosity You’ve probably seen those “Weird Old Tip” ads splashed all over the internet. Although the nature of the business is borderline illegal (and certainly unethical), you can’t deny the pull that these ads have over people. Apply Continuity to Your Pages Before you think I’m trashing the concept of continuity – I’m not.
Data mining - Simple English Wikipedia, the free encyclopedia Data mining is a term from computer science. Sometimes it is also called knowledge discovery in databases (KDD). Data mining is about finding new information in a lot of data. The information obtained from data mining is hopefully both new and useful. In many cases, data is stored so it can be used later. The data is saved with a goal. Later, the same data can also be used to get other information that was not needed for the first use. Finding new information that can also be useful from data, is called data mining. For data, there a lot of different kinds of data mining for getting new information. Pattern recognition (Trying to find similarities in the rows in the database, in the form of rules.
GGobi data visualization system. A Beginner's Guide to A/B Testing: Email Campaigns That Convert Email campaigns and newsletters can be a great way to get repeat business, as well as new customers. You’re already working with a somewhat pre-qualified base: these people have said they want to receive information from you. And a lot of them have likely already done business with you. And we all know it’s easier and cheaper to retain customers than it is to get new ones. This is why it’s vital to run A/B tests when trying out new techniques or formats for your email campaigns. Here’s the third installment in our A Beginner’s Guide to A/B Testing series. Decide What You’ll Test The first step in setting up an effective A/B test is to decide what you’ll test. Call to action (Example: “Buy Now!” Each of those things is likely to have an effect on different parts of the conversion process. Think about this when you’re deciding which things to test first. Test Your Whole List, Or Just Part? In the vast majority of cases, you’ll want to test your entire list. What Does Success Mean?
Evolutionary data mining Process[edit] Data preparation[edit] Before databases can be mined for data using evolutionary algorithms, it first has to be cleaned,[2] which means incomplete, noisy or inconsistent data should be repaired. It is imperative that this be done before the mining takes place, as it will help the algorithms produce more accurate results.[3] At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset.[2] The training dataset will be used to let rules evolve which match it closely.[2] The test dataset will then either confirm or deny these rules.[2] Data mining[edit] This process iterates as necessary in order to produce a rule that matches the dataset as closely as possible.[3] When this rule is obtained, it is then checked against the test dataset.[2] If the rule still matches the data, then the rule is valid and is kept.[2] If it does not match the data, then it is discarded and the process begins by selecting random rules again.[2]
Graphviz Get Found using Inbound Marketing The web has forever changed people’s buying habits. Instead of needing to rely on sales people to send them information, buyers now have Google and other search engines to research products, find competitors, and see how other people rate those products in blogs and reviews. Furthermore they are greatly influenced by individuals that have emerged as experts in particular subject areas who use social media to get their messages across. This sea change in buying behavior requires vendors to re-think how they go to market, and optimize to make sure that they will get found by buyers using search engines, blogs, reviews, and social media. HubSpot produced this great humorous video that highlights the hopelessness of the old techniques in this new world: As further evidence of this change in buying behavior, I was recently talking to the CIO of a large pharmaceutical company, and he told me how he hates spam emails from vendors, and how he had developed a canned email response to them.